Personalized Travel Route Recommendation Using a Hybrid PSO–ACO Algorithm

Personalized Travel Route Recommendation Using a Hybrid PSO–ACO Algorithm | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Personalized Travel Route Recommendation Using a Hybrid PSO–ACO Algorithm Yajun Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8561558/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract With the rapid growth of smart tourism, personalized travel route recommendation has emerged as a critical challenge to enhance user experience and satisfaction. Traditional recommendation systems often fail to adequately capture users' dynamic preferences, sentimental inclinations, and the multifaceted attributes of Points of Interest (POIs). To address these limitations, we propose a hybrid PSO-ACO algorithm that leverages the global search capability of PSO and the local exploitation strength of ACO, while integrating user sentiment and POI similarity into the heuristic function.. The model integrates user sentiment derived from review texts and POI similarity based on rating matrices into an enhanced ACO heuristic function. Furthermore, a PSO module is employed to optimize the initial pheromone distribution of the ACO, thereby accelerating convergence and mitigating the risk of local optima. We validated our approach on a dataset comprising reviews and ratings from 593 tourists for 37 popular scenic spots within Beijing's Forbidden City. Experimental results demonstrate that the proposed PSO-ACO algorithm significantly outperforms benchmark methods, including the elite ant system and improved genetic algorithms, across key metrics of precision, recall, and F1-score. This work provides a robust and personalized framework for intelligent travel itinerary planning. Travel Route Recommendation Ant Colony Optimization (ACO) Particle Swarm Optimization (PSO) Personalized Recommendation Point of Interest (POI) Similarity Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8561558","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615499844,"identity":"37085c44-52ae-4a9f-9544-ae8852cd1b63","order_by":0,"name":"Yajun Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIie3RMQrCMBTG8VeEuAR1TCjoFSKF6hDwIC7tkkknlw4F0yUunkHP0Bu0POiUA3QUujp4AAe76da4Cea//+B9PACf7webQl10TyHnZKwdCS8QBc1UNKGVIxHYqBm1mF5Y4npZY2PgRgWGd2ULudwOiuBs49vSyBEJ1WENjdrrITJidiVSowgJdzELNA4TsrjHrDZICbeOhPa3cG2REUYdCQPECDIlCO23JC5bNlX/ShDyeD1h2T5yOUw+E+6veZNvhc/n8/1HLyRSP5opIeVwAAAAAElFTkSuQmCC","orcid":"","institution":"Zhangjiajie College","correspondingAuthor":true,"prefix":"","firstName":"Yajun","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-01-09 13:38:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8561558/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8561558/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108492164,"identity":"d5fea282-79ee-4149-86d6-877f84e52610","added_by":"auto","created_at":"2026-05-05 09:57:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":393333,"visible":true,"origin":"","legend":"","description":"","filename":"21E12087revised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8561558/v1_covered_fbc90109-65ea-4e99-8821-0d535df805df.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Personalized Travel Route Recommendation Using a Hybrid PSO–ACO Algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Travel Route Recommendation, Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Personalized Recommendation, Point of Interest (POI) Similarity ","lastPublishedDoi":"10.21203/rs.3.rs-8561558/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8561558/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"With the rapid growth of smart tourism, personalized travel route recommendation has emerged as a critical challenge to enhance user experience and satisfaction. Traditional recommendation systems often fail to adequately capture users' dynamic preferences, sentimental inclinations, and the multifaceted attributes of Points of Interest (POIs). To address these limitations, we propose a hybrid PSO-ACO algorithm that leverages the global search capability of PSO and the local exploitation strength of ACO, while integrating user sentiment and POI similarity into the heuristic function.. The model integrates user sentiment derived from review texts and POI similarity based on rating matrices into an enhanced ACO heuristic function. Furthermore, a PSO module is employed to optimize the initial pheromone distribution of the ACO, thereby accelerating convergence and mitigating the risk of local optima. We validated our approach on a dataset comprising reviews and ratings from 593 tourists for 37 popular scenic spots within Beijing's Forbidden City. 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